【摘要】 对MS-Alignment算法进行分析得出该算法很难满足大规模数据对鉴定速度的要求,而且具有的一个特点是相同的任务在不同的数据上重复计算,为数据划分提供了基础。基于CUDA编程模型使用图形处理器(GPU)对步骤数据库检索及候选肽段生成进行加速优化,设计了该步骤在单GPU上的实现方法。测试结果表明,此方法平均加速比为30倍以上,效果良好,可以满足蛋白质翻译后修饰鉴定中大规模数据快速计算的需求。更多还原

【Abstract】 This paper firstly analyzed MS-Alignment. It could not well meet the challenge of large scale data. One of its features was the same computing operations repeat on different data. This feature provided base for data partition. This paper then used GPU ( graphics processing units) to accelerate the step of database search and candidate generation. And it presented an optimized method based on CUDA ( compute unified device architecture) programming model on single GPU. The experimental results show that the average speedup ratio is more than 30,and the method effectively improves identification speed and is applicable for large scale data requiring for high-speed processing.更多还原